{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/jermwatt/machine_learning_refined/blob/main/notes/5_Linear_regression/5_4_Metrics.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BEOiB6k59f4K"
   },
   "source": [
    "## Chapter 5: Linear regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook contains interactive content from an early draft of the university textbook <a href=\"https://github.com/neonwatty/machine-learning-refined/tree/main\">\n",
    "Machine Learning Refined (2nd edition) </a>.\n",
    "\n",
    "The final draft significantly expands on this content and is available for <a href=\"https://github.com/neonwatty/machine-learning-refined/tree/main/chapter_pdfs\"> download as a PDF here</a>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SsI5Tq2j9f4N"
   },
   "source": [
    "# 5.4 Regression Quality Metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "id": "PH8ECrQw9f4N"
   },
   "source": [
    "In this Section we describe simple metrics for judging the quality of a trained regression model, as well as how to make predictions using one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OOADHfuo9f4O",
    "outputId": "138ca34d-f372-4da8-8c07-2344e5fb34e0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: github-clone in /Users/jeremywatt/Desktop/machine-learning-refined/venv/lib/python3.10/site-packages (1.2.0)\n",
      "Requirement already satisfied: requests>=2.20.0 in /Users/jeremywatt/Desktop/machine-learning-refined/venv/lib/python3.10/site-packages (from github-clone) (2.32.3)\n",
      "Requirement already satisfied: docopt>=0.6.2 in /Users/jeremywatt/Desktop/machine-learning-refined/venv/lib/python3.10/site-packages (from github-clone) (0.6.2)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /Users/jeremywatt/Desktop/machine-learning-refined/venv/lib/python3.10/site-packages (from requests>=2.20.0->github-clone) (3.4.0)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /Users/jeremywatt/Desktop/machine-learning-refined/venv/lib/python3.10/site-packages (from requests>=2.20.0->github-clone) (3.10)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/jeremywatt/Desktop/machine-learning-refined/venv/lib/python3.10/site-packages (from requests>=2.20.0->github-clone) (2.2.3)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /Users/jeremywatt/Desktop/machine-learning-refined/venv/lib/python3.10/site-packages (from requests>=2.20.0->github-clone) (2024.12.14)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
      "chapter_5_images already cloned!\n"
     ]
    }
   ],
   "source": [
    "# install github clone - allows for easy cloning of subdirectories\n",
    "!pip install github-clone\n",
    "from pathlib import Path \n",
    "\n",
    "# clone images\n",
    "if not Path('chapter_5_images').is_dir():\n",
    "    !ghclone https://github.com/neonwatty/machine-learning-refined-notes-assets/tree/main/notes/5_Linear_regression/chapter_5_images\n",
    "else:\n",
    "    print('chapter_5_images already cloned!')\n",
    "\n",
    "# append path for local library, data, and image import\n",
    "import sys\n",
    "sys.path.append('./chapter_5_images') \n",
    "\n",
    "# image paths\n",
    "image_path_1 = 'chapter_5_images/Fig_3_5_new.png'\n",
    "\n",
    "# standard imports\n",
    "from IPython.display import Image, HTML\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dA_e1aPa9f4P"
   },
   "source": [
    "## Making predictions using a trained model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dfHWUjxr9f4P"
   },
   "source": [
    "If we denote the optimal set of weights found by minimizing a regression cost function by $\\mathbf{w}^{\\star}$ then note we can write our fully tuned linear model as \n",
    "\n",
    "\\begin{equation}\n",
    "\\text{model}\\left(\\mathbf{x},\\mathbf{w}^{\\star}\\right) =  \\mathring{\\mathbf{x}}_{\\,}^T\\mathbf{w}^{\\star}   = w_0^{\\star} + x_{1}^{\\,}w_1^{\\star} + x_{2}^{\\,}w_2^{\\star} + \\cdots + x_{N}^{\\,}w_N^{\\star}.\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dR51FPRm9f4Q"
   },
   "source": [
    "Regardless of how we determine optimal parameters $\\mathbf{w}^{\\star}$, by minimizing a regression cost like the Least Squares or Least Absolute Deviations, we make predictions employing our linear model in the same way.  That is, given an input (whether one from our training dataset or a new input) $\\mathbf{x}_{\\,}$ we predict its output $y_{\\,}$ by passing it along with our trained weigths into our model as \n",
    "\n",
    "\\begin{equation}\n",
    "\\text{model}\\left(\\mathbf{x}_{\\,},\\mathbf{w}^{\\star}\\right)  = y_{\\,}\n",
    "\\end{equation}\n",
    "\n",
    "This is illustrated pictorially on a prototypical linear regression dataset for the case when $N=1$ in the Figure below.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 292
    },
    "id": "3OdUtZpg9xFV",
    "outputId": "f516d7d1-1fa6-4273-c400-12828e3bb4ff"
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
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     },
     "execution_count": 2,
     "metadata": {
      "image/png": {
       "width": 800
      }
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     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(image_path_1, width=800)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tGlYRRwp9f4R"
   },
   "source": [
    "<figure>\n",
    "<figcaption>   \n",
    "<strong>Figure 4:</strong> <em>  Once a line/hyperplane\n",
    "has been fit to a dataset via minimizing an appropriate cost function\n",
    "it may be used to predict the output value of any input. Here a\n",
    "line has been fit to a two dimensional dataset in this manner, giving\n",
    "optimal parameters $w_0^{\\star}$ and $w_1^{\\star}$, and the output value\n",
    "of a new point $x^{\\prime}$ is made using the learned linear\n",
    "model as $w_0^{\\star}+x^{\\prime}w_1^{\\star} = y^{\\prime}$. </em>  </figcaption> \n",
    "</figure>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xsFQUJgJ9f4S"
   },
   "source": [
    "## Judging the quality of a trained model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Y4K2w6549f4S"
   },
   "source": [
    "Once we have successfully minimized the a cost function for linear regression it is an easy matter to determine the quality of our regression model: we simply evaluate a cost function using our optimal weights.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mCOIspdN9f4S"
   },
   "source": [
    "We can then evalaute the quality of this trained model using a Least Squares cost - which is especially natural to use when we employ this cost in training.  To do this we plug in our trained model and dataset into the Least Squares cost - giving the *Mean Squared Error* (or *MSE* for short) of our trained model\n",
    "\n",
    "\\begin{equation}\n",
    "\\text{MSE}=\\frac{1}{P}\\sum_{p=1}^{P}\\left(\\text{model}\\left(\\mathbf{x}_p,\\mathbf{w}^{\\star}\\right) -y_{p}^{\\,}\\right)^{2}.\n",
    "\\end{equation}\n",
    "\n",
    "The name for this quality measurement describes precisely what the Least Squares cost computes - the average (or *mean*) squared error.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KA2-OteQ9f4S"
   },
   "source": [
    "In the same way we can employ the Least Absolute Deviations cost to determine the quality of our trained model.  Plugging in our trained model and dataset into this cost computes the Mean Absolute Deviations (or *MAD* for short) which is precisely what this cost function computes\n",
    "\n",
    "\\begin{equation}\n",
    "\\text{MAD}=\\frac{1}{P}\\sum_{p=1}^{P}\\left\\vert\\text{model}\\left(\\mathbf{x}_p,\\mathbf{w}^{\\star}\\right) -y_{p}^{\\,}\\right\\vert.\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T54sOg0Z9f4S"
   },
   "source": [
    "These two measurements differ in precisely the ways we have seen their respective cost functions differ - e.g., the MSE measure is far more sensitive to *outliers*.  Which measure one employs in practice can therefore depend on personal and/or occupational preference, or the nature of a problem at hand.\n",
    "\n",
    "Of course in general the *lower* one can make a quality measures, by proper tuning of model weights, the *better* the quality of the corresponding trained model (and vice versa).  However the threshold for what one considers 'good' or 'great'  performance can depend on personal preference, an occupational or institutionally set benchmark, or some other problem-dependent concern."
   ]
  }
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 },
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}
